What factors are contributing to the increased false positive rate in Salt Security's API Attack Detection module this quarter?

Introduction

The increased false positive rate in Salt Security's API Attack Detection module this quarter is a critical issue that demands immediate attention. As we delve into this problem, we'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

Our analysis will follow a structured framework, covering issue identification, hypothesis generation, validation, and solution development. This approach ensures we leave no stone unturned in our quest to resolve the false positive issue and improve the overall performance of our API Attack Detection module.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a recent change in the detection algorithm. Has there been any update to the API Attack Detection module in the last quarter?

Why it matters: Recent changes could directly impact false positive rates. Expected answer: Yes, there was an update to improve detection sensitivity. Impact on approach: If confirmed, we'd focus on the changes made in the update.

• Considering user segments, I'm curious about the distribution of false positives. Are we seeing this increase across all customer types or is it concentrated in specific segments?

Why it matters: This helps identify if the issue is universal or segment-specific. Expected answer: The increase is more pronounced in enterprise customers. Impact on approach: We'd investigate enterprise-specific factors and usage patterns.

• Thinking about the definition of false positives, has there been any change in how we classify or measure these incidents?

Why it matters: Changes in measurement could explain the increase without actual performance degradation. Expected answer: No changes in classification or measurement methods. Impact on approach: We'd focus on actual performance issues rather than measurement discrepancies.

• Considering external factors, have there been any significant changes in the threat landscape or attack patterns in the past quarter?

Why it matters: External changes could necessitate adjustments to our detection algorithms. Expected answer: Some new attack vectors have emerged in the industry. Impact on approach: We'd evaluate our system's ability to adapt to new threats without increasing false positives.